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Sociodemographic Disparities in the Stage of Prostate Cancer Diagnosis in England: A Population-Based Analysis Using Linked Electronic Health Records Data

Somathilake, G; Lemanska, A; Armes, J; Moschoyiannis, S; Ford, E (2025) Sociodemographic Disparities in the Stage of Prostate Cancer Diagnosis in England: A Population-Based Analysis Using Linked Electronic Health Records Data. Studies in Health Technology and Informatics, 327. pp. 1125-1129.

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Abstract

Early-stage cancer diagnosis improves treatment options and prognosis but is influenced by demographic and socio-economic inequalities. This study examined how sociodemographic factors relate to the stage at diagnosis among men aged ≥56 diagnosed with prostate cancer from 2010-2016, using data from the National Cancer Registration and Analysis Service (NCRAS). The differences between groups in the proportions of localised, locally advanced, or advanced cancer stages were analysed using chi-square tests and multinomial logistic regression to assess associations with age, ethnicity, deprivation, and region adjusting for comorbidity and year of diagnosis. Among 13,693 men, 45% had localised, 34% locally advanced, and 21% advanced-stage prostate cancer. Stage at diagnosis varied significantly by age (p<0.001), deprivation (p=0.038), ethnicity (p=0.044), and region (p<0.001). Men who were over 80 years old, compared to men who were 65 and below had an adjusted odds ratio (aOR) of 13.2 (95% confidence interval was 11.0 to 16.0, p<0.001), those from most deprived areas (aOR=1.2 (1.0 to 1.5), p=0.014), and whose registered primary care practice was in the East Midlands (aOR=1.6 (1.2 to 2.2), p<0.001) were more likely to be diagnosed at advanced stages. In contrast, Asian men had lower odds of advanced-stage diagnoses (aOR=0.6 (0.4 to 0.9), p=0.03) compared to White men. These findings underscore the influence of age, deprivation, ethnicity, and region on prostate cancer stage at diagnosis, highlighting the need for more in-depth research and targeted interventions for at-risk groups.

Item Type: Article
Subjects: Mathematics and Scientific Computing > Modelling
Divisions: Data Science
Identification number/DOI: 10.3233/SHTI250565
Last Modified: 10 Jun 2026 12:41
URI: https://eprintspublications.npl.co.uk/id/eprint/10432
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